Multi-Scale Curvelet-Based Directional Denoising for Chest X-Ray Images

In modern healthcare, medical imaging has a significant role in understanding the structure and functioning of the human body, which helps doctors to diagnose, to plan the treatment, and to monitor the disease. Chest X-rays are widely used for the early detection and treatment of various lung infections. The effectiveness and the accuracy of the diagnosis depend largely on the quality of the medical images. Chest X-rays often suffer from Gaussian and Poisson noise, which affects the visibility of fine anatomical structures. Although methods like Gaussian filtering, NLM, and GAN have been used, they often compromise between denoising and retaining edge details. A robust denoising algorithm, Multi-Scale Curvelet Filtering with Directional Denoising (MCF-DD), is proposed to denoise medical chest X-ray images, which uses the curvelet transform coefficients. The performance of the proposed MCF-DD model was evaluated on the Chest X-Ray Images dataset from the Kaggle repository and DICOM images from the MIDRC-RICORD-1C dataset. MCF-DD achieved a PSNR of 36.57dB and SSIM of 0.9062 on Kaggle images, and 40dB PSNR with 0.9412 SSIM on DICOM images, indicating strong denoising performance across both datasets.

@article{Sebastian2026,
  title     = {Multi-Scale Curvelet-Based Directional Denoising for Chest X-Ray Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Neenu Sebastian and B. Ankayarkanni},
  doi       = {10.14569/IJACSA.2026.0170655},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170655}
}